Towards a Personalized Multi-objective Vehicular Traffic Re-routing System
Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems.
de Souza, A. M., Botega, L. C., Garcia, I. C., and Villas, L. A. (2018). Por aqui é mais seguro: Melhorando a mobilidade e a segurança nas vias urbanas. Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 36.
de Souza, A. M., Botega, L. C., and Villas, L. A. (2017a). Gte: Um sistema para gerenciamento de trânsito escalável baseado em compartilhamento oportunista. In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Porto Alegre, RS, Brasil. SBC.
de Souza, A. M., Botega, L. C., and Villas, L. A. (2018a). Fns: Enhancing traffic mobility and public safety based on a hybrid transportation system. In 2018 14th International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 77–84.
de Souza, A. M., Braun, T., Botega, L. C., Cabral, R., Garcia, I. C., and Villas, L. A. (2019a). Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues transportation systems. Springer Journal of Internet Services and Applications, 10(1).
de Souza, A. M., Braun, T., Botega, L. C., Villas, L. A., and Loureiro, A. A. F. (2020a). Safe and sound: Driver safety-aware vehicle re-routing based on spatiotemporal information. IEEE Transactions on Intelligent Transportation Systems, 21(9):3973– 3989.
de Souza, A. M., Braun, T., and Villas, L. (2018b). Efficient contextaware vehicular traffic re-routing based on pareto-optimality: A safe-fast use case. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2905–2910.
de Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., and Villas, L. A. (2017b). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4):1550147716683612.
de Souza, A. M., Maia, G., Braun, T., and Villas, L. A. (2019b). An interest-based approach for reducing network contentions in vehicular transportation systems. Sensors, 19(10).
de Souza, A. M., Oliveira, H. F., Zhao, Z., Braun, T., Villas, L., and Loureiro, A. A. F. (2020b). Enhancing sensing and decision-making of automated driving systems with multi-access edge computing and machine learning. IEEE Intelligent Transportation Systems Magazine, pages 0–0.
de Souza, A. M., Pedrosa, L. L. C., Botega, L. C., and Villas, L. (2018). Itssafe: An intelligent transportation system for improving safety and traffic efficiency. In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pages 1–7.
de Souza, A. M. and Villas, L. A. (2016). A Fully-distributed Traffic Management System to Improve the Overall Traffic Efficiency. In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM ’16, pages 19–26, New York, NY, USA. ACM.
Dong, P., Zheng, T., Yu, S., Zhang, H., and Yan, X. (2017). Enhancing vehicular communication using 5g-enabled smart collaborative networking. IEEEWireless Communications, 24(6):72–79.
Liu, J., Wan, J., Jia, D., Zeng, B., Li, D., Hsu, C., and Chen, H. (2017). Highefficiency urban traffic management in context-aware computing and 5g communication. IEEE Communications Magazine, 55(1):34–40.
Taha, A. and AbuAli, N. (2018). Route planning considerations for autonomous vehicles. IEEE Communications Magazine, 56(10):78–84.
Wedel, J., Schunemann, B., and Radusch, I. (2009). V2x-based traffic congestion recognition and avoidance. In Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on, pages 637–641.
Ye, H., Liang, L., Li, G. Y., Kim, J., Lu, L., andWu, M. (2018). Machine learning for vehicular networks: Recent advances and application examples. IEEE Vehicular Technology Magazine, 13(2):94–101.